Classification of time series by shapelet transformation
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The shapelet transformation classifier (Hills et al., 2014) is an ensemble approach, which makes unclear how the shapelets affect the decision taken by the individual classifiers, whereas for an end-to-end deep learning model the classification can be directly explained by using the Class Activation Map.
“we stress that the shapelet transformation classifier (Hills et al., 2014) is an ensemble approach, which makes unclear how the shapelets affect the decision taken by the individual classifiers whereas for an end-to-end deep learning model we can directly explain the classification by using the Class Activation Map.”
From Deep learning for time series classification: a review (2019) · cites this paper as Hills et al., 2014
Hills et al. (2014) introduced a shapelet transformation based approach that discovered shapelets similar to the ones identified by Ye and Keogh (2011).
“Hills et al. (2014) introduced a shapelet transformation based approach that discovered shapelets that are similar to the ones identified by Ye and Keogh (2011).”
From Deep learning for time series classification: a review (2019) · cites this paper as Hills et al. (2014)
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Deep learning for time series classification: a review
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P.-A. (2019) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-019-00619-1 · Cited by 3,342 · see citing works
“Hence, recent contributions have focused on developing ensemblingmethods that significantly outperforms the NN coupled with DTW (NN-DTW) (Bagnall et al. 2016; Hills et al. 2014; Bostrom and Bagnall 2015; Lines et al. 2016; Schäfer 2015; Kate 2016; Deng et al. 2013; Baydogan et al. 2013).”
BackgroundMethodInfluentialInceptiontime: finding alexnet for time series classification
Ismail Fawaz, H., Lucas, B., Forestier, G., Pelletier, C., Schmidt, D. F., Weber, J., Webb, G. I., Idoumghar, L., Muller, P.-A., & Petitjean, F. (2020) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-020-00710-y · Cited by 1,788 · see citing works
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Explainable Time Series Tweaking via Irreversible and Reversible Temporal Transformations
Karlsson, I., Rebane, J., Papapetrou, P., & Gionis, A. (2018) · 2018 IEEE International Conference on Data Mining (ICDM) · doi:10.1109/icdm.2018.00036 · Cited by 31 · see citing works
- Authors
- Hills, J., Lines, J., Baranauskas, E., Mapp, J., & Bagnall, A.
- Affiliations
- University of East Anglia (GB)
- Corresponding author
- Hills, Jon
- Venue
- Data Mining and Knowledge Discovery
- Published
- 2013-05-18
- Volume
- 28
- Issue
- 4
- Pages
- 851-881
- ISSN
- 1384-5810 (ISSN-L)
- 1573-756X
- OpenAlex
- W1984674851
- Cited by
- 538
- References
- 47
- Citations by year
- 2026: 29 · 2025: 39 · 2024: 46 · 2023: 59 · 2022: 60 · 2021: 66 · 2020: 69 · 2019: 52 · 2018: 35 · 2017: 27 · 2016: 24 · 2015: 21 · 2014: 11
- FWCI
- 29.22
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Data Mining and Knowledge Discovery (publishedVersion)
- UEA Digital Repository (University of East Anglia) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Keywords
- time series classification, shapelets, time series similarity measures, information gain, interpretability, feature extraction
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